Microsoft at ICML 2022

Provable Reinforcement Learning with a Short-Term Memory

Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi

Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback

Fang Kong, Yichi Zhou, Shuai Li

Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models

Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier

Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits

Aadirupa Saha, Shubham Gupta

Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback

Darren Lin, Aldo Pacchiano, Yaodong Yu, Michael Jordan

Analyzing and Mitigating Interference in Neural Architecture Search

Jin Xu, Xu Tan, Kaitao Song, Renqian Luo, Yichong Leng, Tao Qin, Tie-Yan Liu, Sujian Li

DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale

Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He

Equivariant Diffusion for Molecule Generation in 3D

Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, Max Welling

Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments

Anqi Zhang, Jinkun Lin, Aurojit Panda, Jinyang Li, Mathias Lécuyer, Siddhartha Sen

Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution

Vihang Patil, Markus Hofmarcher, Marius-Constantin Dinu, Matthias Dorfer, Patrick Blies, Johannes Brandstetter, Jose A. Arjona-Medina, Sepp Hochreiter

Supervised Off-Policy Ranking

Yue Jin, Yue Zhang, Tao Qin, Xudong Zhang, Jian Yuan, Houqiang Li, Tie-Yan Liu

HousE: Knowledge Graph Embedding with Householder Parameterization

Rui Li, Chaozhuo Li, Jianan Zhao, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, Xing Xie, Qi Zhang

Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms

Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor

Going Deeper into Permutation-Sensitive Graph Neural Networks

Zhongyu Huang, Yingheng Wang, Chaozhuo Li, Huiguang He

Matching Learned Causal Effects of Neural Networks with Domain Priors

Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N Balasubramainan, Amit Sharma

Inductive Biases and Variable Creation in Self-Attention Mechanisms

Benjamin Edelman, Surbhi Goel, Sham Kakade, Cyril Zhang

Bayesian Nonparametric Learning for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations

Fan Yin, Jieying Jiao, Guanyu Hu, Jun Yan

Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness

Adam Foster, Arpi Vezer, Craig Glastonbury, Páidí Creed, Sam Abujudeh, Aaron Sim

Scalable Spike-and-Slab

Niloy Biswas, Lester Mackey, Xiao-Li Meng

Bayesian Nonparametric Learning for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations

Fan Yin, Jieying Jiao, Guanyu Hu, Jun Yan

Analyzing and Mitigating Interference in Neural Architecture Search

Jin Xu, Xu Tan, Kaitao Song, Renqian Luo, Yichong Leng, Tao Qin, Tie-Yan Liu, Sujian Li

DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale

Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, Yuxiong He

Equivariant Diffusion for Molecule Generation in 3D

Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, Max Welling

HousE: Knowledge Graph Embedding with Householder Parameterization

Rui Li, Chaozhuo Li, Jianan Zhao, Di He, Yiqi Wang, Yuming Liu, Hao Sun, Senzhang Wang, Weiwei Deng, Yanming Shen, Xing Xie, Qi Zhang

Going Deeper into Permutation-Sensitive Graph Neural Networks

Zhongyu Huang, Yingheng Wang, Chaozhuo Li, Huiguang He

Matching Learned Causal Effects of Neural Networks with Domain Priors

Sai Srinivas Kancheti, Abbavaram Gowtham Reddy, Vineeth N Balasubramainan, Amit Sharma

Scalable Spike-and-Slab

Niloy Biswas, Lester Mackey, Xiao-Li Meng

Provable Reinforcement Learning with a Short-Term Memory

Yonathan Efroni, Chi Jin, Akshay Krishnamurthy, Sobhan Miryoosefi

Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution

Vihang Patil, Markus Hofmarcher, Marius-Constantin Dinu, Matthias Dorfer, Patrick Blies, Johannes Brandstetter, Jose A. Arjona-Medina, Sepp Hochreiter

Supervised Off-Policy Ranking

Yue Jin, Yue Zhang, Tao Qin, Xudong Zhang, Jian Yuan, Houqiang Li, Tie-Yan Liu

Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments

Anqi Zhang, Jinkun Lin, Aurojit Panda, Jinyang Li, Mathias Lécuyer, Siddhartha Sen

Simultaneously Learning Stochastic and Adversarial Bandits with General Graph Feedback

Fang Kong, Yichi Zhou, Shuai Li

Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms

Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor

Contrastive Mixture of Posteriors for Counterfactual Inference, Data Integration and Fairness

Adam Foster, Arpi Vezer, Craig Glastonbury, Páidí Creed, Sam Abujudeh, Aaron Sim

Inductive Biases and Variable Creation in Self-Attention Mechanisms

Benjamin Edelman, Surbhi Goel, Sham Kakade, Cyril Zhang

Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models

Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier

Optimal and Efficient Dynamic Regret Algorithms for Non-Stationary Dueling Bandits

Aadirupa Saha, Shubham Gupta

Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback

Darren Lin, Aldo Pacchiano, Yaodong Yu, Michael Jordan

Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum

Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama

On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features

Jinxin Zhou, Xiao Li, Tianyu Ding, Chong You, Qing Qu, Zhihui Zhu

Contextual Bandits with Large Action Spaces: Made Practical

Yinglun Zhu, Dylan Foster, John Langford, Paul Mineiro

Data Augmentation as Feature Manipulation

Ruoqi Shen, Sébastien Bubeck, Suriya Gunasekar

Understanding Contrastive Learning Requires Incorporating Inductive Biases

Nikunj Umesh Saunshi, Jordan Ash, Surbhi Goel, Dipendra Kumar Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, Akshay Krishnamurthy

SE(3) Equivariant Graph Neural Networks with Complete Local Frames

weitao du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Tie-Yan Liu, Nanning Zheng, Bin Shao

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization

Alberto Bietti, Chen-Yu Wei, Miro Dudík, John Langford, Steven Wu

Interactively Learning Preference Constraints in Linear Bandits

David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause

RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests

Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, Vasilis Syrgkanis

Finding Global Homophily in Graph Neural Networks When Meeting Heterophily

Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, Weining Qian

PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance

Qingru Zhang, Simiao Zuo, Chen Liang, Alexander Bukharin, Pengcheng He, Weizhu Chen, Tuo Zhao

Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods

Yi Wan, Harm van Seijen, Ida Momennejad, Sarath Chandar, Janarthanan Rajendran, Ali Rahimi-Kalahroudi

Sparsity in Partially Controllable Linear Systems

Yonathan Efroni, Sham Kakade, Akshay Krishnamurthy, Cyril Zhang

Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers

Rui Liu, Young Jin Kim, Alexandre Muzio, Hany Hassan Awadalla

PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance

Qingru Zhang, Simiao Zuo, Chen Liang, Alexander Bukharin, Pengcheng He, Weizhu Chen, Tuo Zhao

Adaptive Inertia: Disentangling the Effects of Adaptive Learning Rate and Momentum

Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato, Masashi Sugiyama

On the Optimization Landscape of Neural Collapse under MSE Loss: Global Optimality with Unconstrained Features

Jinxin Zhou, Xiao Li, Tianyu Ding, Chong You, Qing Qu, Zhihui Zhu

Data Augmentation as Feature Manipulation

Ruoqi Shen, Sébastien Bubeck, Suriya Gunasekar

Understanding Contrastive Learning Requires Incorporating Inductive Biases

Nikunj Umesh Saunshi, Jordan Ash, Surbhi Goel, Dipendra Kumar Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, Akshay Krishnamurthy

SE(3) Equivariant Graph Neural Networks with Complete Local Frames

weitao du, He Zhang, Yuanqi Du, Qi Meng, Wei Chen, Tie-Yan Liu, Nanning Zheng, Bin Shao

Finding Global Homophily in Graph Neural Networks When Meeting Heterophily

Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, Weining Qian

Gating Dropout: Communication-efficient Regularization for Sparsely Activated Transformers

Rui Liu, Young Jin Kim, Alexandre Muzio, Hany Hassan Awadalla

Personalization Improves Privacy-Accuracy Tradeoffs in Federated Optimization

Alberto Bietti, Chen-Yu Wei, Miro Dudík, John Langford, Steven Wu

Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods

Yi Wan, Harm van Seijen, Ida Momennejad, Sarath Chandar, Janarthanan Rajendran, Ali Rahimi-Kalahroudi

Sparsity in Partially Controllable Linear Systems

Yonathan Efroni, Sham Kakade, Akshay Krishnamurthy, Cyril Zhang

Contextual Bandits with Large Action Spaces: Made Practical

Yinglun Zhu, Dylan Foster, John Langford, Paul Mineiro

Interactively Learning Preference Constraints in Linear Bandits

David Lindner, Sebastian Tschiatschek, Katja Hofmann, Andreas Krause

RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests

Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, Vasilis Syrgkanis

Branching Reinforcement Learning

Yihan Du, Wei Chen

An Intriguing Property of Geophysics Inversion

Yinan Feng, Yinpeng Chen, Shihang Feng, Peng Jin, Zicheng Liu, Youzuo Lin

A Framework for Learning to Request Rich and Contextually Useful Information from Humans

Khanh Nguyen, Yonatan Bisk, Hal Daumé III

NeuralEF: Deconstructing Kernels by Deep Neural Networks

Zhijie Deng, Jiaxin Shi, Jun Zhu

Visual Attention Emerges from Recurrent Sparse Reconstruction

Baifeng Shi, Yale Song, Neel Joshi, Xin Wang, Trevor Darrell

Adversarially Trained Actor Critic for Offline Reinforcement Learning

Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal

Lie Point Symmetry Data Augmentation for Neural PDE Solvers

Johannes Brandstetter, Max Welling, Daniel E. Worrall

Transformers are Meta-Reinforcement Learners

Luckeciano Carvalho Melo

Universal and data-adaptive algorithms for model selection in linear contextual bandits

Vidya Muthukumar, Akshay Krishnamurthy

Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences

Aadirupa Saha, Pierre Gaillard

NP-Match: When Neural Processes meet Semi-Supervised Learning

Jianfeng Wang, Thomas Lukasiewicz, Xiaolin Hu, Daniela Massiceti, Vladimir Pavlovic, Alexandros Neophytou

On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces

Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil, Brian Sadler, Pratap Tokekar, Alec Koppel

Asking for Knowledge (AFK): Training RL Agents to Query External Knowledge Using Language

Iou-Jen Liu, Xingdi Yuan, Marc-Alexandre Côté, Pierre-Yves Oudeyer, Alex Schwing

A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1

Adil Salim, Lukang Su, Peter Richtarik

An Intriguing Property of Geophysics Inversion

Yinan Feng, Yinpeng Chen, Shihang Feng, Peng Jin, Zicheng Liu, Youzuo Lin

NP-Match: When Neural Processes meet Semi-Supervised Learning

Jianfeng Wang, Thomas Lukasiewicz, Xiaolin Hu, Daniela Massiceti, Vladimir Pavlovic, Alexandros Neophytou

NeuralEF: Deconstructing Kernels by Deep Neural Networks

Zhijie Deng, Jiaxin Shi, Jun Zhu

Visual Attention Emerges from Recurrent Sparse Reconstruction

Baifeng Shi, Yale Song, Neel Joshi, Xin Wang, Trevor Darrell

Lie Point Symmetry Data Augmentation for Neural PDE Solvers

Johannes Brandstetter, Max Welling, Daniel E. Worrall

A Convergence Theory for SVGD in the Population Limit under Talagrand's Inequality T1

Adil Salim, Lukang Su, Peter Richtarik

A Framework for Learning to Request Rich and Contextually Useful Information from Humans

Khanh Nguyen, Yonatan Bisk, Hal Daumé III

Adversarially Trained Actor Critic for Offline Reinforcement Learning

Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal

Transformers are Meta-Reinforcement Learners

Luckeciano Carvalho Melo

On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces

Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil, Brian Sadler, Pratap Tokekar, Alec Koppel

Asking for Knowledge (AFK): Training RL Agents to Query External Knowledge Using Language

Iou-Jen Liu, Xingdi Yuan, Marc-Alexandre Côté, Pierre-Yves Oudeyer, Alex Schwing

Branching Reinforcement Learning

Yihan Du, Wei Chen

Universal and data-adaptive algorithms for model selection in linear contextual bandits

Vidya Muthukumar, Akshay Krishnamurthy

Versatile Dueling Bandits: Best-of-both World Analyses for Learning from Relative Preferences

Aadirupa Saha, Pierre Gaillard

Modeling the Data-Generating Process is Necessary for Out-of-Distribution Generalization

Jivat Neet Kaur, Emre Kiciman, Amit Sharma